Title: "Orthogonalisation methods for fast computing of Bayesian model selection" Supervisor: David Rossell
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This repository is used for personal use, and to share my work done this year at Warwick with the Github community.
In the Big Data generation, performing Bayesian model selection in a computationally fast manner is a main challenge in Statistics. In this work we explore methods to orthogonalise a general form Gram matrix XTX, and employ them to carry out scalable Bayesian model selection and averaging in a linear regression context. For this purpose, both PCA-related techniques and the novel DECO method are examined, for increasing p and correlation amongst the predictors. Finally, we consider both situations where the number of variables p is small and large.
Keywords: Model Selection, Decorrelation, Bayesian Model Averaging, Shrinkage, Sparse PCA.